Exploring health professionals' views on the depiction of conversational agents as health professionals: a qualitative descriptive study
Bibliographic record
Abstract
Background: Some health care conversational agents (HCCAs) are designed to simulate health professionals in terms of their presentation or appearance. Research suggests that the public has favorable views toward the depiction of HCCAs as health professionals, but the views of health professionals are less clear. We conducted a qualitative descriptive study to learn more about health professionals' views on this topic. Methods: Physicians, nurses, and regulated mental health professionals were recruited using web-based methods. Participants were interviewed individually using the Zoom videoconferencing platform. They were asked to discuss potential benefits and drawbacks surrounding the depiction of HCCAs as health professionals. Interviews were transcribed verbatim and uploaded to NVivo (version 12; QSR International, Inc) for thematic analysis. Results: = 10.71). Three themes were developed from their interview data. Participants said that portraying HCCAs as health professionals is a form of misrepresentation and may mislead program users. Participants were also concerned that these depictions could draw from stereotypes regarding the appearance of health professionals, which might affect people's expectations surrounding these programs or their willingness to use them. Despite these concerns, some participants thought that there may be benefits to depicting HCCAs as health professionals, particularly in terms of providing a sense of reassurance to people seeking health support. Conclusions: The health professionals in this study expressed mixed views toward the depiction of HCCAs as health professionals. Their insights may prompt further discussion on the appropriate depiction of HCCAs among developers and other stakeholders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".